The proliferation of internet-connected smart-building devices (e.g. thermostats and motion sensors) has led to devices frequently reporting indications of human presence. Early building automation devices (e.g. motion sensors) often relied on built-in sensors to detect occupants. Many such devices neither received data from nor contribute data to a building-wide network. Later, building automation devices were improved with networking capabilities to enable data gathering (e.g. proximity card entry systems and Zigbee sensor networks). Recently, there has been a proliferation of user-installed smart home devices. A related area of development is building-scale operating systems (OS), wherein sensor data (often from smart devices) is aggregated, processed and the resulting incites are offered as shared services to client devices in the smart building. For example, a building OS can gather sensor data, estimate the number of occupants and continuously make this up-to-date occupancy estimate available to client devices such as security systems, lighting systems, thermostats, photocopiers, smart televisions, and security cameras. Client devices can thereby better adapt to people, based on shared knowledge of the number of people in the building.
A related and arguably more difficult challenge is to continuously estimate the number of people in a dynamically defined region within a building. Commercial buildings often have electronic badge readers at the entrances for security. These are ineffective at estimating the number of occupants because they typically only register when people enter the building and people can hold the door open for others. Retail stores can have entry and exit counters at the store entrance. These are ineffective at estimating the number of occupants because multiple people can enter at once and appear to be a single person.
The outer perimeter of a building is one logical boundary within which to count the number of people but there are circumstances where it is advantageous to count the people in a dynamically defined region within a building. Equipment that counts people at building entrances is ineffective at estimating the number of people in a dynamically defined region of the building (e.g. on a particular floor or a range of office numbers). A real-time estimate of the number of occupants in a selectable region of a building is also useful for fire safety, and provisioning heating ventilation and air conditioning (HVAC) resources.
Buildings have long had automation based on occupant detectors for energy savings (e.g. motion sensors), but these do not count occupants. An example of the deficiency associated with not counting people is the classic problem automated lighting in a conference room turning off when occupants are present but stationary. Knowing the number of people in a building or regions thereof could improve the performance of automated lighting and similar automation devices and services. For example, when a person moves from one region to another motion activated lights are typically programmed with a long delay (e.g. 1-2 minute) before turning off because the system cannot discount the presence of a second stationary occupant remaining behind after the first person has left the region. An improved automated lighting system could use a continuous estimate of the number of people in the vicinity to respond more intelligently. For example, a lighting system could determine that only a single occupant is in a region of the building and could thereby track the sole occupant and turn off lights quickly behind them, because additional occupants do not have to be accounted for.
One challenge that sensor networks face when estimating the number of occupants in a building is that several sensors can detect the same occupant at once (e.g. two motion sensors or cameras with overlapping fields of view). This problem is called co-site or aliasing. It is difficult for a network of sensors to differentiate between simultaneous detection of a single occupant with multiple sensors and multiple occupants on several sensors. Hence as far as I am aware no method previously disclosed effectively provides a realtime estimate of the number of people in a selectable region of a building.